1. Field of the Invention
The present invention is directed to visualization methods and, more particularly, to volume-rendering techniques.
2. Description of the Background
The increasing availability of powerful workstations has fueled the development of new methods for visualizing or rendering volumetric datasets. Volumetric datasets are scalar or vector density fields defined over a 3D grid. The individual scalar values at each grid point is called a voxel. Typically, volumetric datasets are available from many different sources such as:
medical scanners such as magnetic resonance imagers (MRI) and computed tomography (CT);
sound spectrum analyzers which may produce seismic data;
laser strip triagulators which may produce height field data; and
fluid dynamics data from discretization of 3D Navier-Stokes' partial-differential equations describing fluid flow.
Astrophysical, meteorological and geophysical measurements, and computer simulations using finite element models of stress, fluid flow, etc., also quite naturally generate a volumetric dataset. Given the current advances in imaging devices and computer processing power, more and more applications will generate volumetric datasets in the future. Unfortunately, it is difficult to see the 3-dimensional structure of the interior of volumes by viewing individual slices. To effectively visualize volumes, it is important to be able to image the volumes from different view points.
There are a number of visualizations methods which fall under the category of volume-rendering techniques. In certain of these techniques, a color and an opacity are assigned to each voxel, and a 2D projection of the resulting colored semitransparent volume is computed. One of the advantages of volume rendering is that operations such as cutting, slicing, or tearing, while challenging for surface based models, can be performed easily with a volumetric representation. While slicing is possible on traditional 3D models, the lack of any information on the internal structure means that no new information is to be had by slicing and viewing the internals. Another drawback of these techniques is their computational cost. Because all voxels participate in the generation of each image, rendering time grows linearly with the size of the dataset. As a result, real-time imaging becomes problematic with large datasets.
Real-time interactivity, however, is crucial for volumetric rendering. One requirement of volume rendering applications is the need to classify the volume into sub-regions each representing homogenous density values. In medical imaging, that ensures that anatomically different regions are rendered distinctly from one another. For example, classification enables a surgeon to separate, without ambiguity, nerve endings from the surrounding soft-tissue or the white matter from the gray matter in an image of the human brain. In geophysics and mining, it ensures that rock strata of incrementally different densities are clearly delineated in the rendering process. And in archaeology, it enables the archaeologist to easily resolve small density differences such as between fossilized bone and attached rock matrix.
Color and opacity texture lookup tables are central to classification. That allows the user to define isodensity regions of the volume dataset to be mapped to the same color and opacity. However, oftentimes anatomically distinct regions are not entirely homogeneous. Typically, an anatomically distinct region of the volumetric dataset will occupy a range of density values. The problem is to identify this range accurately. While statistical methods that assign the opacity and color to a voxel based on the probability that a particular tissue component is present in a tissue are available to ensure that classification can be done with a quantifiable degree of accuracy, methods of classifications based on visually interactive means present the user with a quick way of deriving acceptable results. Even sophisticated methods of classification based on multispectral and multichannel data ultimately fine tune the classification by having the user guide the assignment of the opacity functions based on visual feedback.
Human perceptual studies have shown that the human eye is sharply sensitive to intensity changes in visual images. The need exists to enable quick visual updates of volume rendered images, preferably without a time-lag, when the user defines updates to the color and opacity lookup tables. Such an ability would provide the user with a tool that allows the user to track the resulting intensity changes in the image interactively. Such real-time visual feedback is key to enabling the user to quickly identify the boundaries of the regions of interest. A trained surgeon or a geophysicist may use such a tool with a remarkable degree of accuracy to demarcate the boundaries of an observed region of interest. From a usability point of view then, such a feature is an absolute requirement for ensuring good analysis of the dataset.